Stop Friendly Fraud: AI Tactics to Cut Chargebacks

Chargebacks aren’t “just a payments problem.” They’re a customer service problem that shows up on your P&L.

Visa has estimated that up to 75% of chargebacks may be fraudulent, often categorized as friendly fraud—when a customer disputes a legitimate purchase instead of working with the merchant to resolve it. Meanwhile, merchants report the trend is accelerating: in the 2024 Chargeback Field Report, 72% of merchants said invalid disputes increased, with an average rise of 18% over the prior period.

Here’s the uncomfortable part: your contact center is either preventing chargebacks—or quietly producing them. When customers can dispute a transaction in seconds inside a banking app, any friction in support (long waits, unclear answers, confusing refund rules) nudges them toward the “Dispute” button. And fraudsters know this. They probe agents, script conversations, and exploit weak authentication.

This post is part of our AI in Payments & Fintech Infrastructure series, where the theme is simple: payments outcomes (fraud, routing, authorizations, disputes) are increasingly driven by the systems around the transaction. Contact centers sit right in that blast radius. If you want fewer chargebacks, you need better service and better controls—and AI can help with both.

Why chargebacks hit harder than refunds (and why support owns it)

A refund is usually a controlled, two-way exchange: money back, merchandise back, relationship intact. A chargeback is the opposite: it’s adversarial, slow, and expensive.

Merchants typically lose in three ways:

• Revenue + inventory loss: the disputed funds are pulled back, and the item often isn’t returned.
• Direct fees: chargeback fees commonly range $20–$100 per dispute, and they’re often not returned even if you win.
• Operational drag: disputes can take weeks or months, consuming time across support, finance, fraud, and ops.

This matters for contact center leaders because chargebacks create a hidden workload multiplier:

• Every dispute triggers evidence gathering (order records, tracking, transcripts, chat logs).
• Agents and supervisors get pulled into escalations.
• WISMO (“where is my order”) and refund-status contacts spike during peak seasons—exactly when your queue is already strained.

My take: most companies treat chargebacks as a back-office function and then wonder why they keep rising. The earlier you intervene—before the dispute—the cheaper and cleaner the outcome.

The contact center is a fraud surface (not just a help desk)

Fraudsters like contact centers because humans are easier to manipulate than payment gateways.

Social engineering that fuels friendly fraud

Some callers aren’t trying to solve a problem—they’re trying to collect just enough detail to file a “credible” dispute. Others aim for the double win: pressure an agent into a refund and then file a chargeback anyway.

AI-assisted coaching can reduce this risk by:

• flagging high-pressure language patterns (“I need this refunded right now or I’ll call my bank”)
• surfacing policy snippets and “say/do” guidance in real time
• detecting when an agent is about to disclose unnecessary transaction details

Account takeover (ATO) via weak verification

If your authentication is still mostly knowledge-based (“name, address, last order amount”), you’re leaving a side door open. ATO often looks like a normal call: change the email, update the phone number, reroute shipping, “I forgot my password.” Then the real cardholder notices and disputes.

AI can help by using risk-based authentication inside the workflow:

• escalate to stronger verification when signals stack up (new device, unusual geolocation, rushed behavior)
• recommend step-up checks (one-time passcode, verified email link, in-app confirmation)
• identify repeat patterns across calls that humans won’t connect

Data exposure and call recording risk

Call recordings can accidentally become a sensitive-data archive if you’re not careful.

AI-enabled redaction and compliance controls can:

• automatically detect and mask payment card data and identity tokens
• restrict playback/download based on role and risk
• alert security teams when sensitive content appears where it shouldn’t

The goal isn’t to make agents’ jobs harder. It’s to make the safe path the easy path.

The fastest way to cut chargebacks: remove “support friction”

The best chargeback is the one that never gets filed. The pattern behind many disputes is boring: unclear descriptors, shipment delays, refund confusion, and long wait times.

Aite-Novarica (via TSYS) has reported that clearer transaction details could reduce call volume by about 25%. That’s not just a staffing win—it’s a dispute-prevention strategy.

Fix the top three “I don’t recognize this” triggers

Chargebacks often start with: “I don’t recognize the charge.” That’s frequently your fault, not the customer’s.

Practical improvements that reduce disputes:

• Billing descriptor hygiene: match your descriptor to your storefront name and include a support phone number when possible.
• Proactive digital receipts: send a confirmation that includes item name, delivery expectations, cancellation link, and a “need help?” entry point.
• Self-serve order lookup: let customers find orders by email/phone without forcing account creation.

AI comes in when you scale these fixes:

• automatically classify incoming contacts as “descriptor confusion,” “subscription renewal,” or “family member purchase”
• generate recommended responses and next steps based on policy and order context
• route “high-risk” confusion cases to faster queues

Make speed measurable: two metrics that matter

If disputing in a banking app takes under a minute, your support experience has to feel comparably frictionless.

Two operational targets I like for chargeback prevention:

1. Answer time: aim for under 2 minutes, with a stretch goal closer to 20 seconds for top dispute drivers (billing questions, delivery issues, refunds).
2. One-contact resolution (OCR): reduce transfers and repeat contacts on refund and shipment problems.

AI can help by:

• predicting queue spikes (especially around holiday shipping cutoffs and post-holiday returns)
• offering virtual agents for “where is my refund/order” questions
• summarizing prior interactions so the next agent doesn’t restart from zero

AI that actually helps: three use cases that pay for themselves

Plenty of teams buy “AI for customer service” and end up with a chatbot that apologizes a lot and solves little. For chargebacks, you want AI that changes outcomes.

1) Sentiment + intent detection to catch disputes before they happen

Answer first: sentiment analysis helps you find customers likely to escalate to chargebacks while there’s still time to save the transaction.

A practical flow:

• The model detects rising frustration (negative sentiment, threat language, repeated contact).
• The case is tagged “dispute risk.”
• The customer is offered a faster path: immediate refund, expedited replacement, or a human callback.

This is where contact center AI ties directly into payments infrastructure: you’re not just “handling a ticket,” you’re preventing a downstream financial loss.

2) Real-time fraud flags during live interactions

Answer first: fraud detection in the contact center should work like fraud detection at checkout—quietly scoring risk and triggering step-up checks.

Signals that can be scored in real time:

• mismatch between caller ID region and shipping address history
• repeated attempts to change email/phone plus urgent refund request
• multiple accounts using the same device fingerprint or contact details
• unusual call cadence (many short calls probing policy)

When risk is high, AI should recommend a specific action, not vague warnings:

• “Require one-time passcode to the file-on-record phone.”
• “Block address change; create a security case.”
• “Do not disclose transaction details beyond what’s visible in the customer portal.”

3) Automated evidence assembly for representment

Even with great prevention, you’ll still fight some disputes.

Answer first: AI should reduce the time-to-respond by turning scattered artifacts into a clean evidence packet.

Instead of hunting across systems, AI can compile:

• order timeline (purchase, fulfillment, delivery)
• customer communications (chat/email/call summaries)
• refund policy acceptance and timestamps
• device and login history (where appropriate)

This doesn’t just cut labor—it improves win rates because evidence is consistent, complete, and on time.

A practical “chargeback prevention playbook” for contact centers

If you want fewer chargebacks in 2026, don’t start with tools. Start with decisions.

Step 1: Define what agents can do without approval

Empowerment reduces disputes because it reduces delay.

Set clear thresholds where frontline agents can:

• issue refunds
• offer store credit
• resend items
• waive shipping

If every meaningful resolution requires a supervisor, you’ve built a system that pushes customers toward banks.

Step 2: Build omnichannel continuity (not “more channels”)

Omnichannel isn’t about adding SMS or social DMs. It’s about continuity—the customer shouldn’t have to repeat themselves.

AI can unify threads by:

• matching identities across channels
• summarizing prior context instantly
• recommending consistent policy outcomes across voice, chat, and email

Step 3: Add risk-based authentication where it counts

Not every contact needs heavy verification. High-risk actions do.

Protect:

• account detail changes
• refund to a new payment method
• address changes close to shipment
• password resets with unusual signals

Layer simple step-up options: one-time codes, verified email links, or in-app approvals.

Step 4: Use branded calling to improve callback pickup

Outbound calls can stop disputes—if customers answer.

Branded caller ID helps customers recognize your business, making them more likely to respond to:

• delivery exception outreach
• refund confirmation
• subscription renewal reminders

This is one of those “small” contact center changes that shows up as a meaningful chargeback reduction later.

What to measure (so this doesn’t become another AI project)

If you’re running AI in a contact center with a chargeback goal, track outcomes that finance will respect.

A tight metrics set:

• Chargebacks per 1,000 orders (trend by product line and channel)
• Pre-dispute contact rate (how often customers contact you before disputing)
• Refund cycle time (request-to-completion)
• Repeat contact rate for top dispute drivers
• High-risk action step-up rate (how often you required stronger auth)

A good sign you’re on track: more customers contact you first, and fewer contacts end in threats to dispute.

Where this fits in AI in Payments & Fintech Infrastructure

AI in payments isn’t only about better fraud models at authorization. It’s also about reducing the messy, expensive exceptions after the fact—disputes, chargebacks, and manual reviews.

Contact centers are one of the highest-leverage places to do that because they sit at the intersection of:

• customer experience (speed, clarity, fairness)
• identity and authentication
• post-transaction resolution

If you treat chargebacks like a bank-only problem, you’ll keep paying the chargeback tax. If you treat them like an end-to-end system problem, your contact center becomes a revenue protection function.

If you want a practical next step: map your last 100 chargebacks to the customer’s support journey. How many tried to contact you first? How many got stuck in refunds or delivery confusion? How many involved account access changes? The patterns will be obvious—and AI can target them.

The real question heading into 2026: Will your customers find your support experience faster than their bank’s dispute button?